Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation

Cowling, Peter I.; Kendall, Graham; Soubeiga, Eric

doi:10.1007/3-540-46004-7_1

Cited by 71 publications

(70 citation statements)

References 12 publications

(27 reference statements)

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“…It is described thus: "The hyperheuristics manage the choice of which lower-level heuristic method should be applied at any given time, depending upon the characteristics of the heuristics and the region of the solution space currently under exploration." The approach has been used successfully to solve a sales summit scheduling problem [5], [6] and a project presentation scheduling problem [7]. In their approach, they first design a general framework for a hyperheuristic to select which low-level heuristic to apply, and further improve their approach using a choice function.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Length Chromosome Hyper-Heuristic Genetic Algorithm for a Trainer Scheduling Problem

Han¹,

Kendall²,

Cowling³

2004

Recent Advances in Simulated Evolution and Learning

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Hyper-GA was introduced by the authors as a genetic algorithm based hyperheuristic which aims to evolve an ordering of low-level heuristics so as to find a good quality solution to a given problem. The adaptive length chromosome hyper-GA, let's call it ALChyper-GA, is an extension of the authors previous work, in which the chromosome was of fixed length. The aim of a variable length chromosome is two fold; 1) it allows dynamic removal and insertion of heuristics 2) it allows the GA to find a good chromosome length which could otherwise only be found by experimentation. We apply the ALChyper-GA to a geographically distributed training staff and courses scheduling problem, and report that good quality solution can be found. We also present results for four versions of the ALChyper-GA, applied to five test data sets.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Length Chromosome Hyper-Heuristic Genetic Algorithm for a Trainer Scheduling Problem

Han¹,

Kendall²,

Cowling³

2004

Recent Advances in Simulated Evolution and Learning

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“…[20,21] are the earliest studies testing simple heuristic selection methods as a selection hyper-heuristic component. One of the most basic and preliminary approaches to select low level heuristics is the Simple Random (SR) approach requiring no learning at all.…”

Section: Heuristic Selection and Move Acceptance Methodologiesmentioning

confidence: 99%

A tensor-based selection hyper-heuristic for cross-domain heuristic search

Asta

Özcan

2015

Information Sciences

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Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance methods under an iterative single point-based search framework. At each step, the solution in hand is modified after applying a selected heuristic and a decision is made whether the new solution is accepted or not. In this study, we represent the trail of a hyper-heuristic as a third order tensor. Factorization of such a tensor reveals the latent relationships between the low level heuristics and the hyper-heuristic itself. The proposed learning approach partitions the set of low level heuristics into two equal subsets where heuristics in each subset are associated with a separate move acceptance method. Then a multi-stage hyper-heuristic is formed and while solving a given problem instance, heuristics are allowed to operate only in conjunction with the associated acceptance method at each stage. To the best of our knowledge, this is the first time tensor analysis of the space of heuristics is used as a data science approach to improve the performance of a hyper-heuristic in the prescribed manner. The empirical results across six different problem domains from a benchmark indeed indicate the success of the proposed approach.

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“…This search process is based on generation or selection of the right low level heuristics to efficiently solve the problem at hand, rather than solving the problem directly (Burke et al, 2003;Burke et al, 2013;Ozcan et al, 2013). This way, it can be achieved a near optimal solution using cheap and easy-toimplement low-level heuristics, instead of using an expensive computational approach (Cowling et al, 2002).…”

Section: Hyper-heuristicsmentioning

confidence: 99%

Selection constructive based hyper-heuristic for dynamic scheduling

Gomes

Madureira

Cunha

2015

2015 10th Iberian Conference on Information Systems and Technologies (CISTI)

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ii iii ResumoA função de escalonamento desempenha um papel importante nos sistemas de produção. Os sistemas de escalonamento têm como objetivo gerar um plano de escalonamento que permite gerir de uma forma eficiente um conjunto de tarefas que necessitam de ser executadas no mesmo período de tempo pelos mesmos recursos. Contudo, adaptação dinâmica e otimização é uma necessidade crítica em sistemas de escalonamento, uma vez que as organizações de produção têm uma natureza dinâmica. Nestas organizações ocorrem distúrbios nas condições requisitos de trabalho regularmente e de forma inesperada. Alguns exemplos destes distúrbios são: surgimento de uma nova tarefa, cancelamento de uma tarefa, alteração na data de entrega, entre outros. Estes eventos dinâmicos devem ser tidos em conta, uma vez que podem influenciar o plano criado, tornando-o ineficiente. Portanto, ambientes de produção necessitam de resposta imediata para estes eventos, usando um método de reescalonamento em tempo real, para minimizar o efeito destes eventos dinâmicos no sistema de produção. Deste modo, os sistemas de escalonamento devem de uma forma automática e inteligente, ser capazes de adaptar o plano de escalonamento que a organização está a seguir aos eventos inesperados em tempo real.Esta dissertação aborda o problema de incorporar novas tarefas num plano de escalonamento já existente. Deste modo, é proposta uma abordagem de otimização -Hiper-heurística baseada em Seleção Construtiva para Escalonamento Dinâmico-para lidar com eventos dinâmicos que podem ocorrer num ambiente de produção, a fim de manter o plano de escalonamento, o mais robusto possível. Esta abordagem é inspirada em computação evolutiva e hiper-heurísticas. Do estudo computacional realizado foi possível concluir que o uso da hiper-heurística de seleção construtiva pode ser vantajoso na resolução de problemas de otimização de adaptação dinâmica. Palavras AbstractScheduling plays an important role in manufacturing systems. It produces a scheduling plan, in order to share resources to produce several different products in the same time period. However, dynamic adaptation and optimization is a critical need in real-world manufacturing scheduling systems, since contemporary manufacturing organizations have a dynamic nature, where disturbances on working conditions and requirements occur on a continuous basis. Disturbances often arise unexpectedly, and can be for example: urgent job arrival, job cancelation, due date change, delay in the arrival, among others. These dynamic events must be taken into account, since they may have a major impact on the scheduling plan, they can disorder the plan making it ineffective. Therefore, manufacturing environments require immediate response to these dynamic events, using a real-time rescheduling method, in order to minimize the effect of such unexpected events in the performance of the production' system. As result, scheduling systems should have the ability of automatically and intelligently maintain real-time adaptation and optimization to effi...

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Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation

Cited by 71 publications

References 12 publications

An Adaptive Length Chromosome Hyper-Heuristic Genetic Algorithm for a Trainer Scheduling Problem

An Adaptive Length Chromosome Hyper-Heuristic Genetic Algorithm for a Trainer Scheduling Problem

A tensor-based selection hyper-heuristic for cross-domain heuristic search

Selection constructive based hyper-heuristic for dynamic scheduling

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